Object store management operations within compute-centric object stores

ABSTRACT

Object store management operations within compute-centric object stores are provided herein. An exemplary method may include transforming an object storage dump into an object store table by a table generator container, wherein the object storage dump includes at least objects within an object store that are marked for deletion, transmitting records for objects from the object store table to reducer containers, such that each reducer container receives object records for at least one object, the object records comprising all object records for the at least one object, generating a set of cleanup tasks by the reducer containers, and executing the cleanup tasks by a cleanup agents.

CROSS REFERENCE TO RELATED APPLICATION

This non-provisional patent application claims the benefit of, and is acontinuation of, U.S. patent application Ser. No. 13/836,599 titled“Object Store Management Operations Within Compute-Centric ObjectStores,” filed Mar. 15, 2013, now U.S. Pat. No. 8,775,485, issued onJul. 8, 2014, which is hereby incorporated by reference herein in itsentirety including all references cited therein.

FIELD OF THE PRESENT TECHNOLOGY

The present technology relates generally to the execution of objectstore management operations within a compute-centric object store. Thepresent technology leverages compute operations within a compute-centricobject store to facilitate management operations such as garbagecollection, resource metering, and so forth.

BACKGROUND

Various methods and systems for providing multitenant computing systems,such as cloud computing, have been attempted. In general, a cloud-basedcomputing environment is a resource that typically combines thecomputational power of a large model of processors and/or that combinesthe storage capacity of a large model of computer memories or storagedevices. For example, systems that provide a cloud resource may beutilized exclusively by their owners; or such systems may be accessibleto outside users who deploy applications within the computinginfrastructure to obtain the benefit of large computational or storageresources.

The cloud may be formed, for example, by a network of servers with eachserver (or at least a plurality thereof) providing processor and/orstorage resources. These servers may manage workloads provided bymultiple users (e.g., cloud resource consumers or other users).Typically, each user places workload demands upon the cloud that vary inreal-time, sometimes dramatically. The nature and extent of thesevariations typically depend on the type of business associated with theuser.

Management operations within these systems comprise, for example,garbage collection, where the system cleans up object stores bydeleting, consolidating, or otherwise processing user deleted data.Garbage collection may include deleting or updating dependencies betweenobjects, namely objects that were deleted by users that were formerlyassociated with other objects in the object store. Other managementoperations comprise usage metering, where the system generates reportsthat are indicative of system resource utilization. These reports may begenerated to provide system administrators with empirical data regardingobject store usage, even down to the user level. Unfortunately, objectstorage systems that utilize these management functions often leveragethird party tools, which are not specifically designed for the objectstore itself. Thus, administrators may struggle with compatibilityissues and other similar drawbacks.

SUMMARY OF THE PRESENT TECHNOLOGY

According to some embodiments, the present technology may be directed tomethods that comprise: (a) transforming an object storage dump into anobject store table by a table generator container, wherein the objectstorage dump includes at least objects within an object store that aremarked for deletion; (b) transmitting records for objects from theobject store table to reducer containers, such that each reducercontainer receives object records for at least one object, the objectrecords comprising all object records for the at least one object; (c)generating a set of cleanup tasks by the reducer containers; and (d)executing the cleanup tasks by a cleanup agents.

According to some embodiments, the present technology may be directed tosystems that comprise: (a) one or more processors; and (b) logic encodedin one or more tangible media for execution by the one or moreprocessors, the logic comprising: (i) a table generator container thattransforms an object storage dump into an object store table, whereinthe object storage dump includes at least objects within an object storethat are marked for deletion; (ii) a set of reducer containers that:receive records for objects from the object store table from the tablegenerator, such that each reducer container receives object records forat least one object, the object records comprising all object recordsfor the at least one object; (iii) generate a set of cleanup tasks; and(iv) cleanup agents that execute the cleanup tasks.

According to some embodiments, the present technology may be directed tomethods that comprise: (a) partitioning raw usage records by a map phasecontainer; (b) grouping the partitioned records by user, type, and anidentifier via a first set of reducer containers; (c) deduplicating thegrouped usage records via a second set of reducer containers; (d)aggregating the deduplicated usage records for a user for a namespaceinto namespace records via the second set of reducer containers; and (e)summing namespace records for each namespace associated with the userinto a usage report via a third set of reducer containers.

According to some embodiments, the present technology may be directed tosystems that comprise: (a) one or more processors; and (b) logic encodedin one or more tangible media for execution by the one or moreprocessors, the logic comprising: (i) a map phase container thatpartitions raw usage records; (ii) a first set of reducer containersthat groups partitioned records by user, type, and an identifier; (iii)a second set of reducer containers that deduplicate the grouped usagerecords; (iv) the second set of reducer containers aggregating thededuplicated usage records for a user for a namespace into namespacerecords; and (iv) a third set of reducer containers that each sum thenamespace records for each namespace associated with a user into usagereports.

BRIEF DESCRIPTION OF THE DRAWINGS

Certain embodiments of the present technology are illustrated by theaccompanying figures. It will be understood that the figures are notnecessarily to scale and that details not necessary for an understandingof the technology or that render other details difficult to perceive maybe omitted. It will be understood that the technology is not necessarilylimited to the particular embodiments illustrated herein.

FIG. 1 is a block diagram of an exemplary architecture in whichembodiments of the present technology may be practiced;

FIG. 2 is a schematic diagram of an exemplary guest virtual operatingsystem container;

FIG. 3 is a schematic diagram illustrating the colocation of guestvirtual operating system containers for multiple tenants on an objectstore;

FIG. 4 is a schematic diagram of a guest virtual operating systemcontainer applied onto an object store;

FIG. 5A illustrates an exemplary arrangement of virtual operating systemcontainers that are utilized to facilitate an exemplary garbagecollection process;

FIG. 5B is a flowchart of an exemplary process for garbage collection;

FIG. 5C illustrates an exemplary arrangement of virtual operating systemcontainers that are utilized to facilitate an exemplary meteringprocess;

FIG. 5D is a flowchart of an exemplary process for metering; and

FIG. 6 illustrates an exemplary computing system that may be used toimplement embodiments according to the present technology.

DESCRIPTION OF EXEMPLARY EMBODIMENTS

While this technology is susceptible of embodiment in many differentforms, there is shown in the drawings and will herein be described indetail several specific embodiments with the understanding that thepresent disclosure is to be considered as an exemplification of theprinciples of the technology and is not intended to limit the technologyto the embodiments illustrated.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the presenttechnology. As used herein, the singular forms “a”, “an” and “the” areintended to include the plural forms as well, unless the context clearlyindicates otherwise. It will be further understood that the terms“comprises” and/or “comprising,” when used in this specification,specify the presence of stated features, integers, steps, operations,elements, and/or components, but do not preclude the presence oraddition of one or more other features, integers, steps, operations,elements, components, and/or groups thereof.

It will be understood that like or analogous elements and/or components,referred to herein, may be identified throughout the drawings with likereference characters. It will be further understood that several of thefigures are merely schematic representations of the present technology.As such, some of the components may have been distorted from theiractual scale for pictorial clarity.

FIG. 1 is a block diagram of an exemplary architecture 100 in whichembodiments of the present technology may be practiced. The architecture100 comprises a plurality of client devices 105A-N that communicativelycouple with a compute-centric object store system, hereinafter “system110.” It will be understood that the architecture 100 may include aplurality of systems, such as system 110. For the sake of brevity andclarity, a detailed description of an exemplary system 110 will beprovided below, although the features of the system 110 apply equally toall of the plurality of systems. The plurality of client devices 105A-Nmay communicatively couple with the system 110 via any one orcombination of a number of private and/or public networks, such as theInternet. According to some embodiments, the client devices 105A-N maysubmit requests or jobs to a network service 110B, which is aconstituent part of the system 110. In some instances, the networkservice 110E evaluates request received from users to determine one ormore physical nodes that comprise objects that correspond to therequest.

In general, the system 110 comprises an object store 110A that provides“compute” as a first class citizen of an object store 110A. Morespecifically, compute operations (e.g., instructing the system tocompute on objects in the object store) of the present technologyresemble a top-level API function, similar to processes like storing orfetching objects in the object store 110A.

It will be understood that the terms “object store” comprise a networkservice for storing unstructured, arbitrary-sized chunks of data(objects). It will be further understood that the object store may notsupport modifications to existing objects, but supports full objectreplacement operations, although systems that support both objectmodification and full object replacement operations may also utilize thefeatures of the present technology to perform compute operationsdirectly on (e.g., in-situ) objects within the object store.

In some embodiments, the system 110 may be configured to receive arequest to perform a compute operation on at least a portion of anobject store, from a first user. Again, the user may be associated withone of the client devices. The request identifies parameters of thecompute operation as well as objects against which the compute operationis executed.

In some instances, the system 110 may assign virtual operating systemcontainers to a user, based upon a request. The system 110 may mapobjects to the containers that are associated with the user. Typically,these objects are identified by the user in the request. A virtualoperating system container performs the compute operation on an objectaccording to the identified parameters of the request. The system 110may then clear the virtual operating system containers and return thevirtual operating system containers to a pool of virtual operatingsystem containers. Additional aspects of the system 110 will bedescribed in greater detail below.

It will be understood that a compute-centric object store may be createdto operate without the user of virtual operating system (global kernel)or virtual operating system containers. While such an object store wouldprovide advantages such as in-situ computation of data (where objectsare processed directly on the object store), the object store may notisolate tenants in the similarly to systems that utilize a virtualoperating system and/or virtual operating system containers.

In these instances, the compute-centric object store may be configuredto receiving a request to perform a compute operation on at least aportion of an object store from a first user via a network service, therequest identifying parameters of the compute operation. The objectstore may also execute an operating system process for the objectsidentified in the request. The operating system process may perform thecompute operation on the object according to the identified parametersof the request. Additionally, once the compute operation has beenexecuted, the operating system process may be terminated by the virtualoperating system.

The terms in-situ computation will be understood to include theexecution of compute operations against objects in an object store,where the objects not moved or copied from or within the object store.

In some embodiments, the system 110 is comprised of a hardware layer 115that provides a logical interface with at least one or more processorsand a memory which stores logic that is executed by the one or moreprocessors. Generally, the hardware layer 115 controls one or more ofthe hardware components of a computing system, such as the computingsystem 600 of FIG. 6, which will be described in greater detail below.By way of non-limiting example, the hardware layer 115 may manage thehardware components of a server blade or another similar device. Thehardware layer 115 provides access to the physical hardware thatservices a global operating system kernel 120 that cooperates with thehardware layer 115. The global operating system kernel 120 may also bereferred to as a host operating system kernel.

Generally, the global operating system kernel 120 is configured toadminister and manage a pool of guest virtual operating systemcontainers, such as containers 125A-N. The containers 125A-N may operateon a distributed object store in a multitenant manner, where multiplecontainers can operate on the same object store simultaneously. It willbe understood that each user is assigned container from the pool, on anas-needed basis. When a container is applied to an object store thecontainer is referred to as a tenant.

According to some embodiments, the system kernel 120 may be utilized tosetup the pool of guest virtual operating system containers. The systemkernel 120 may also be configured to provide a command line interpreterinterface that allows users to request jobs, execute other operatingsystem implemented applications, and interact with a virtual operatingsystem in a manner that is substantially indistinguishable relative toan operating system executing on a bare metal device.

Generally, a job may be input by a user via a command line interpreter,such as a Unix shell terminal. More specifically, the user may express acomputation using the same language as the language used by a Unix shellterminal. The actual request is submitted to the network service 110B.Indeed, a request may be submitted as an HTTP request to the networkservice 110B. The body of the request describes the computation toperform in terms of what commands are input into the command lineinterpreter, which is running within a container. Contrastingly systemsthat utilize multiple VMs that each comprises an operating systemkernel, which are managed by a hypervisor, often require users toconstruct complex programs or scripts to perform compute operations.Compute operations for traditional VM systems require complexprogramming due to a complex framework that is used by the hypervisor tocoordinate hardware emulation for each of the VMs.

Using the command line interpreter interface, the user may specify oneor more desired compute operations that are to be executed againstobjects (such as object 130) within an object store 110A (see FIG. 3).It is noteworthy that the object store 110A may include, for example, alocal or distributed object store that maintains contiguous blobs,blocks, or chunks of data. It will be understood that the objects storedin the object store 110A are complete objects, such as files or othersimilar data structures. Moreover, the compute operations executedagainst the object store 110A may be performed in such a way thatpartial stores of data are avoided.

In order to perform compute operations on objects for multiple users,the system kernel 120 may collocate containers 125A-N onto the objectstore 110A, and execute the containers 125A-N simultaneously. In FIG. 3,a plurality of containers, such as container 125A has been placed ontoeach of a plurality of objects within the object store 110A. Thus, avirtual operating system container is assigned to each of the pluralityof objects specified in the user request. Most frequently, theassignment of a single container to a single object occurs when thesystem executes a “map” phase operation. The details of map and reducephases provide by the system 110 will be described in greater detailbelow.

Broadly speaking, a virtual operating system container may be alightweight virtualization solution offering a complete and secure userenvironment that operates on a single global kernel (system kernel 120),providing performance characteristics that are similar to operatingsystems that operate on bare metal devices. That is, a virtual machineoperates on emulated hardware and is subject to control by a hypervisor,which produces computing inefficiencies. A virtual operating systemcontainer may operate without the computing inefficiencies of a typicalvirtual machine.

In some instances, the system kernel 120 may utilize a KVM (KernelVirtual Machine) that improves the efficiency of the a virtual operatingsystem, such as the global operating system kernel, by leveraging CPUvirtualization extensions to eliminate a substantial majority of thebinary translation (i.e., hardware emulation) that are frequentlyrequired by VMs.

Turning to FIG. 2, an exemplary virtual operating system container 125A(FIG. 1) is shown as comprising a quick emulator layer (QEMU) 135, avirtual guest operating system 140, and a compute application 145 thatis managed by the virtual guest operating system 140. The QEMU 135provides hardware emulation and is also VMM (virtual machine monitor).It is noteworthy that in some embodiments the QEMU 135 is not a stricthypervisor layer, but rather each QEMU 135 may be independent in someexemplary embodiments. That is, there may be one QEMU 135 one percontainer instead of a single QEMU 135 supporting several VMs.Advantageously, the operations of both a VM and a VMM may be combinedinto the QEMU 135.

According to some embodiments, the compute application 145 that isexecuted may include a primitive O/S compute operation. Exemplarycompute operations may include operating system primitive operations,such as query, word count, send, receive, and so forth. Additionally,the operations may comprise more sophisticated operations, such asoperations that include audio or video transcoding. Additionally, insome instances, users may store programs or applications in the objectstore itself. Users may then execute the programs as a part of a computeoperation.

In some instances the compute operations may include one or more phasessuch as a map phase, followed by a reduce phase. Generally, a map phasemay include an operation that is executed against each of a plurality ofobjects individually, by a plurality of containers. In some instances, aunique container is assigned to each object that is to be processed.

In contrast, a reduce phase may be executed by a single containeragainst a plurality of objects in a batch manner. Using an example suchas word count, it will be assumed that the objects of the object store110A may comprise text files. The compute application 145 may execute amap phase to count the words in each of the text files. The output ofthe compute application 145 may be stored in a plurality of outputobjects that are stored in the object store 110A. A compute application145 of another container may execute a reduce phase that sums the outputobjects of the map phase and generates a word count for all objectswithin the object store 110A.

It will be understood that the system kernel 120 may schedule andcoordinate various compute operations (and phases) performed by thecompute applications 145 of all containers. In sum, the system kernel120 may act similarly to a hypervisor that manages the computeoperations of the various active containers. Based upon the requestinput by the user, the system kernel 120 may instruct the containers toperform a series of map functions, as well as a reduce functions. Themap and reduce functions may be coordinated to produce the desiredoutput specified in the request.

Turning to FIG. 3, after receiving a request from a user, the systemkernel 120 may select a first set of containers, which includescontainer 125A from the pool of containers. This container 125A isassigned to a user. In response to receiving a request from a seconduser, the system kernel 120 may also select a second set of containersfrom the pool of containers.

Based upon the request received from the first tenant, the system kernel120 may map the first set of containers to a plurality of objects, suchas object 130, stored in the object store 110A. Likewise, the systemkernel 120 may map a second set of containers to a plurality ofdifferent objects stored in the object store 110A for the second user.The objects and containers for the first user may be referred to as acompute zone of the first user, while the objects mapped to thecontainer 125N may be referred to as a compute zone of the second user.The maintenance of compute zones allows the system kernel 120 to providemultitenant access to the object store 110A, even when the first andsecond users are potentially adversarial. For example, the first andsecond users may be commercial competitors. For security, the systemkernel 120 maintains compute zones in order to balkanize object storageand prevent access to objects of other users. Additionally, thebalkanization of object storage also ensures fair distribution ofresources between users.

It will be understood that the system kernel 120 may maintain as manycontainers and compute zones as allowed by the processor(s) of thehardware layer 115. Additionally, the system kernel 120 assigns acontainer to a user on an as-needed basis, meaning that containers maynot be assigned permanently to a user, which would result in amonopolization of resources when the user is not performing computeoperations.

FIG. 4 illustrates the placement of the container 125A onto the objectstore 110A. It is understood that the container 125A encircles aplurality of objects in the object store 110A. This mapping of multipleobject to a single container would be commonly seen in a reduce phase,where the container is performing a concatenating or summation processon the outputs of individual containers, such as the containers shown inFIG. 3.

Additionally, because the container is placed onto the object store, thesystem kernel 120 need not transfer objects from the object store 110Ainto the container for processing in some exemplary embodiments.Advantageously, the container operates directly on the objects of theobject store 110A.

According to some embodiments, the containers 125A-N managed by thesystem kernel 120 are empty when the containers 125A-N are in the pool.After objects are mapped to the container, compute operations may beexecuted by the container on the objects, and a desired output isgenerated, the system kernel 120 may clear the container and return thecontainer to the pool.

In some instances, the system kernel 120 may not generate containersuntil a request is received from a user. That is, the system kernel 120may “spin up” or launch containers when a request is received from theuser. This allows for minimum impact to the bare metal resources, suchas the CPU, as the system kernel 120 need not even maintain a pool ofvirtual operating system containers, which are awaiting user requests.That is, maintaining a pool of containers requires CPU and memoryresources. When the compute operations have been completed, the systemkernel 120 may terminate the containers, rather than clearing thecontainers and returning the containers to a pool.

In accordance with the present disclosure, an instruction setarchitecture may be implemented within the system 110. In someembodiments, the instruction set architecture may specify an applicationprogramming interface that allows the system 110 to interact with thedistributed object store.

According to some embodiments, the system 110 communicatively coupleswith the object store 110A using a services related applicationprogramming interface (SAPI) 155, which provides features such asautomatic discovery of object stores, dynamic configuration of objectstores, and an API for a user portal. In sum, the SAPI allows users toconfigure, deploy, and upgrade applications using a set ofloosely-coupled, federated services. In some embodiments, the SAPI mayinclude an underlying API and an autoconfig agent, also referred to as adaemon 150. A SAPI client may also be disseminated to clients. It willbe understood that the daemon 150 may be associated with a physical node160 of the object store 110A.

In accordance with some embodiments according to the present disclosure,various object stores, such as object store 110A of FIGS. 3 and 4,comprise a single SAPI zone. It will be understood that the SAPI zonemay be stateless and the SAPI zone may be configured to write objectsinto the object store 110A. In addition to storing objects, the SAPIzone may also communicatively couple with a VM API to provision zonesand a network API (NAPI) to reserve network interface controllers (NIC)and lookup network universal unique identifiers (UUID).

It will be understood that the SAPI 155 may comprise three main objecttypes such as applications, services, and instances. It is noteworthythat an application may comprise one or more services, and each servicemay comprise one or more instances. Moreover, instances may representactual object store zones, and such zones inherit zone parameters andmetadata from their associated applications and services.

Also, the application, service, and instance information may be used bythe compute application of a virtual operating system container that isplaced onto an object store. The daemon 150 may control the operation ofthe containers operating on the daemon's object store.

Each application, service and instance may include three sets ofproperties. For example, “params” may comprise zone parameters like azone's RAM size, disk quota, image UUID, and so forth. These parametersare evaluated when a zone is provisioned. Another property comprises“metadata”, which defines metadata available to the daemon 150. Thesemetadata keys and values form the input of a script template in aconfiguration manifest (described below). As these values are updated,the daemon 150 may rewrite any configuration and make reference tochanged metadata values. Yet another property comprises “manifests” thatdefine a set of configuration manifests are indexed by name tofacilitate inheriting manifest from parent objects.

It is noteworthy that creating applications and services have no effecton running zones. When an instance is created, a zone is provisionedusing the above information from its associated application, service,and instance. Stated otherwise, applications and services (e.g., a jobor request) may be defined separate from the objects that theapplications and services are to be executed against. Thus, a job may bethought of abstractly as a workflow template. Advantageously, when theuser requests the execution of a job, objects need only be defined bythe user. The workflow template is then applied against the objects.

In some embodiments, the daemon 150 of a zone may be tasked withmaintaining configuration inside that zone. The daemon 150 queries theSAPI 155 directly to determine which files to write and where to writethem within the object store 110A.

The daemon 150 uses objects called configuration manifests; thoseobjects describe the contents, location, and semantics of configurationfiles for a zone. Those manifests contain a script template which isrendered using the metadata from the associated application, service,and instance.

When a user provides a request to the system 110, the system kernel 120may coordinate a compute flow of compute operations which are managed bythe daemon 150. That is, the system kernel 120 may receive a request or“job” from a user, via a command line interpreter. The requestidentifies parameters of a compute operation that is to be executedagainst objects in a distributed object store. For example, a requestmay include performing a word count operation on a file.

To facilitate compute flow during the compute process, the system kernel120 may assign an identifier for the request. This identifier provides aunique identifier that allows objects and outputs of compute operationsto be correlated to the user. Objects previously stored in the objectstore may be correlated to the user utilizing a unique identifier.According to some embodiments, the identifier comprises the name of aninput object or job name. This name may be specified by an end usersubmitting a job/request to the system or may be generated by the systemfrom the request.

The user may also identify objects for the compute operation, using, forexample, the command line interpreter. An exemplary find object commandmay include Find|User|Object Store Location; where the Object StoreLocation defines the object store that includes the object(s) which arenecessary for execution of the compute operation.

In some instances, the system kernel 120 may query various daemons ofobject stores to locate the objects within the distributed object store.After the object have been located, the system kernel 120 may generate aset of tasks (e.g., an instruction set) that defines the various computeoperations that are to be performed by the daemon of the located objectstore. In the example provided above, the set of tasks may include onlyone word count task that is provided to a single daemon of an objectstore (e.g., physical node). This relatively simple compute operationdoes not require coordination or scheduling of operations of multipleobjects.

The daemon 150 may provide instructions to one or more virtual operatingsystem containers that are placed onto the object store by the systemkernel 120. That is, the instruction sets provided to the containers isbased upon the task assigned to the daemon 150 from the system kernel120.

In some instances, the set of tasks may include a more complexarrangement of operations that are executed against a plurality ofobjects stores. The system kernel 120 may interact with the daemon tocoordinate processing of these objects in a specified order.

Additionally, the set of tasks may define various map phases that are tobe executed on the objects of the object store, as well as variousreduce phases that are executed on the outputs of the map phases. Itwill be understood that objects within the workflow may be tracked andcorrelated together using the identifier. For example, if an instructionset passed to a daemon requires performing a word count computeoperation on 100 text files, each of the objects of the computeoperation would be correlated using the identifier. Thus, the objects ofthe compute operation would comprise 100 objects that each includes aword count value for their corresponding text file. The identifier maybe appended to the object as metadata.

It will also be understood that a map phase may result in multipleoutputs, which are generated from a single input object. For example,assume that usage logs for a computing device are stored for a 24 hourtime period. To determine hourly usage rates, the 24 hour log object maybe separated into 24 distinct objects. Thus, the map phase may receivethe 24 hour log object and may split the same into constituent outputobjects to complete the map phase.

It will be understood that a more complex request may require a morecomplicated set of tasks (e.g., phases). For example, if the userdesires to look at all 11 p.m. to 12 p.m. user logs for a plurality ofcomputing devices, the set of tasks may require not only the map taskwhere a single input object is processed into multiple objects, but alsoa reduce phase that sums a plurality of 11 p.m. to 12 p.m. user logs fora plurality of devices.

In sum, the system kernel 120 will provide a daemon with tasks thatinclude a map phase for generating the hour increment logs from variousinput objects. Additionally, the tasks also inform the daemon to returnthe output objects, which may be stored as an aggregate 11 p.m. to 12p.m. log object within the object store.

It will be understood that the daemon of a physical node (e.g., objectstore) may control execution of compute operations by the one or morevirtual operating system containers that are placed onto the objectstore via the system kernel 120.

Thus, it is appreciated with that intermediate output objects may not beoutput to the user directly, but may be fed back into the system foradditional processing, such as with the map and reduce phases describedabove. Moreover, the set of tasks generated by the system kernel 120 mayinclude any number of map phases and/or reduce phases, which varyaccording to the steps required to produce the desired output.

According to some embodiments, the system may leverage virtual operatingsystem containers or compute zones to execute object store managementfunctions within the compute-centric object store. Generally, thesemanagement functions comprise garbage collection, usage metering, and soforth. While many other management functions may be implemented, thefollowing examples will further describe the execution of garbagecollection and usage metering. One of ordinary skill in the art willappreciate that other object store management functions may alsolikewise be implemented using the frameworks described below, butconfigured to execute the desired function.

Broadly speaking, garbage collection refers to processes utilized toclean up an object store after a series of garbage creation events. Agarbage creation event may include, for example, when a user deletes anobject, or when a user overwrites an object that is linked to one ormore dependent objects. Garbage created by these events may include thedeleted object, as well as any orphan links created by the deletion,where an orphan link includes a link between the deleted object and oneor more dependent objects.

The following definitions provide context for the following discussion.A storage node is a physical server that stores actual user data, andhas at least one virtual operating system container pooled collocatedwith the storage node.

An internal metadata system is a place attributes about data ownership,sizes, free space, and so forth are stored. This internal metadatasystem is comprised of shards. These shards comprise data structuresthat have been partitioned in such a way that the system can add morepartitions to the shared as the system grows.

Turning to the garbage collection process, it will be understood thateach time a user uploads and subsequently deletes data within an objectstore; garbage is left over in the system in the form of files onstorage nodes. If not dealt with, the system may generate enough garbageto cause the system to run out of room for new data within a shortperiod of time.

As background, the system may utilize an indexing tier (e.g.,authoritative store), which is an authoritative source that tracks whichobjects within the system of object stores and informs the system as towhich objects are live. Objects are live, in the sense that the objectsare accessible for retrieval through a system API (ApplicationProgramming Interface). Since the indexing tier is an authoritativestore, the indexing tier is consulted to determine if a particularobject should be collected. The indexing tier is actually composed ofmany shards, with each shard being responsible for a slice of one ormore live objects. References for files in the storage node can exist onany of the shards. When an object is associated with multiple shards,each of the associated shards is consulted. Due to the nature ofdistributed systems, all shards cannot be consulted at the same instantin time. Due to this limitation, garbage collection is implemented as ajob post-processing the set of shards.

FIG. 5A illustrates an exemplary arrangement of virtual operating systemcontainers that are utilized to facilitate an exemplary garbagecollection process. A table generator container 505 is executed totransform an object storage dump into an object store table 505A. Theobject storage dump includes objects within the object store that havebeen marked for deletion. Objects marked for deletion may be indexed ina log record 505B stored on the object store. The first field in eachrecord may include the object identifier; the second field is the timethe record was produced.

The table generator container 505 may transmit records for each objectto a set of reducers 510 in such a way that each reducer is provided allrecords for at least one given object. It will be understood that areducer may receive all object records for a plurality of objects. Thereducers sort the set of rows so that records for objects are groupedtogether and ordered by time. Each reducer may walk the history of anobject (review records in chronological order) and decide if the objectshould be garbage collected. An object is garbage collected if the onlyrecord of that object is a deletion record that has passed a system widegrace period. In addition, deletion records are cleaned up if recordsfor the object referenced in the deletion record have later records.

The output of the reducers 510 is a set of cleanup tasks for shards andobject storage nodes. Cleanup tasks may then be processed by uploadedthe tasks to a directory within the object store. Additionally, anoperations file is uploaded with the set of commands to run to link thecleanup files into the correct locations for a set of crons. Thesecommands may be uploaded to another specified task directory 520 withinthe object store.

Generally speaking, a cron is a basic Unix facility for executingarbitrary programs periodically (e.g., usually hourly, daily, weekly,etc.). A “cronjob” is process which is set up to run periodically withcron. Cronjobs may gather and upload a component's last hour's log filesto the object store itself, both for long-term log storage or immediateprocessing using a compute component. In some instances, the cronsinclude cleanup agents 515 that run within a compute zone. Specifically,the compute zone is referred to as a cleanup zone, when the cron is acleanup agent.

Cleanup agents 515 will periodically look in a directory 520 wherecleanup tasks are stored and the cleanup agents 515 will periodicallyexecute the cleanup tasks. The cleanup agents 515 look for files inspecified directories, downloads the files, and processes each line ofthe file to delete objects from the object store. The cleanup agents 515execute within a cleanup zone so that it can reach out “directly” toeach shard.

A cleanup agent will execute on each node and the cleanup agent willlook for objects in a specified location on the node. The cleanup agentwill then download the task for the object and processes each line ofthe object to delete object off the local disk.

FIG. 5B is a flowchart of the method 525 executed by the arrangement ofFIG. 5A. The method includes a step 525A of transforming an objectstorage dump into an object store table. The object storage dumpincludes objects within the object store that have been marked fordeletion. Objects marked for deletion may be indexed in a log recordstored on the object store.

Next the method may include a step 525B of generating cleanup tasks foreach deleted object from the object store table via a set of reducers.Each deleted object may include a separate record within the objectstore table. Additionally, the method may include a step 525C ofexecuting the cleanup tasks via a set of cleanup agents. A cleanup agentmay be assigned for each deleted object. The cleanup agent processes thecleanup task for the object by evaluating each line of the object todelete the objects from the object store.

FIG. 5C illustrates an exemplary arrangement of virtual operating systemcontainers that are utilized to facilitate a metering process.Generally, metering reports are generated by running various jobs withinthe system. Daily reports are generated from hourly reports, and monthlyreports may be generated from daily reports. There are three classes ofusage that may be measured: (1) Storage: bytes stored on disk, number ofkeys, number of objects, and so forth; (2) Request: request totals bymethod, total transfer (bandwidth) in and out; (3) Compute: tasks thatare executed by users.

With regard to storage, daily storage metering sums up the usage fromevery hour on the given calendar day, and monthly storage metering sumsup the usage from every day on the given calendar month. This meansdaily and monthly storage metering represent unit-hours of usage (i.e.,byte-hours, key-count-hours).

Storage hourly metering consists of a map phase and three reduce phases.Storage raw data 530 consists of dumps of data to each shard. The firstline contains a table name and describes the schema for each entry.

The map phase container 535 takes extracts a ‘_value’ field and emits itas output. These records are partitioned 540 and sent to a first set ofreducer containers 545, grouped by owner, type, and objectId such thatall identifiers that point to the same object for a single owner arecollocated on a single reducer. Each reducer container 545 deduplicatesany keys that point to the same object and aggregates usage per owner bynamespace. ObjectIds are indexed in memory and any keys that point tothe same object only increment a user's number of keys. The reducercontainers 545 output deduplicated records 550.

The second set of reducer containers 555 sums up all of a user's usagefor a single namespace. The output format is the same as the outputformat of the first reduce phase. Results are partitioned by owner.Cardinality of data is number of users multiplied by the number ofnamespaces. Once aggregation is finished, one aggregated record 560 isemitted per owner per namespace partitioned to that reducer.

The third set of reducer containers 565 combines a usage acrossdifferent namespaces for a single user into a single record. If a userhas no usage for a namespace, zero usage is added to the record for thatnamespace at this time. The third set of reducer containers 565 deliversreports 570 to each user's reports namespace.

FIG. 5D is a flowchart of a method for metering object store usage. Themethod 575 may include a step 575A of partitioning raw usage records anda step 575B of grouping partitioned records by owner, type, andidentifier. The method may also include a step 575C of deduplicatinggrouped usage record entries. The method may include a step 575D ofaggregating the deduplicated usage records for a user for a namespaceinto namespace records. The method also includes a step 575E of summingnamespace records for each namespace associated with the user.

FIG. 6 illustrates an exemplary computing system 600 that may be used toimplement an embodiment of the present systems and methods. Thecomputing system 600 of FIG. 6 may be implemented in the contexts of thelikes of computing systems, networks, servers, or combinations thereof.The computing system 600 of FIG. 6 includes one or more processors 610and main memory 620. Main memory 620 stores, in part, instructions anddata for execution by processor 610. Main memory 620 may store theexecutable code when in operation. The computing system 600 of FIG. 6further includes a mass storage device 630, portable storage device 640,output devices 650, user input devices 660, a display system 670, andperipheral devices 680.

The components shown in FIG. 6 are depicted as being connected via asingle bus 690. The components may be connected through one or more datatransport means. Processor 610 and main memory 620 may be connected viaa local microprocessor bus, and the mass storage device 630, peripheraldevice(s) 680, portable storage device 640, and display system 670 maybe connected via one or more input/output (I/O) buses.

Mass storage device 630, which may be implemented with a magnetic diskdrive or an optical disk drive, is a non-volatile storage device forstoring data and instructions for use by processor 610. Mass storagedevice 630 may store the system software for implementing embodiments ofthe present technology for purposes of loading that software into mainmemory 620.

Portable storage device 640 operates in conjunction with a portablenon-volatile storage medium, such as a floppy disk, compact disk,digital video disc, or USB storage device, to input and output data andcode to and from the computing system 600 of FIG. 6. The system softwarefor implementing embodiments of the present technology may be stored onsuch a portable medium and input to the computing system 600 via theportable storage device 640.

User input devices 660 provide a portion of a user interface. User inputdevices 660 may include an alphanumeric keypad, such as a keyboard, forinputting alpha-numeric and other information, or a pointing device,such as a mouse, a trackball, stylus, or cursor direction keys.Additional user input devices 660 may comprise, but are not limited to,devices such as speech recognition systems, facial recognition systems,motion-based input systems, gesture-based systems, and so forth. Forexample, user input devices 660 may include a touchscreen. Additionally,the computing system 600 as shown in FIG. 6 includes output devices 650.Suitable output devices include speakers, printers, network interfaces,and monitors.

Display system 670 may include a liquid crystal display (LCD) or othersuitable display device. Display system 670 receives textual andgraphical information, and processes the information for output to thedisplay device.

Peripherals device(s) 680 may include any type of computer supportdevice to add additional functionality to the computer system.Peripheral device(s) 680 may include a modem or a router.

The components provided in the computing system 600 of FIG. 6 are thosetypically found in computer systems that may be suitable for use withembodiments of the present technology and are intended to represent abroad category of such computer components that are well known in theart. Thus, the computing system 600 of FIG. 6 may be a personalcomputer, hand held computing system, telephone, mobile computingsystem, workstation, server, minicomputer, mainframe computer, or anyother computing system. The computer may also include different busconfigurations, networked platforms, multi-processor platforms, etc.Various operating systems may be used including Unix, Linux, Windows,Mac OS, Palm OS, Android, iOS (known as iPhone OS before June 2010),QNX, and other suitable operating systems.

It is noteworthy that any hardware platform suitable for performing theprocessing described herein is suitable for use with the systems andmethods provided herein. Computer-readable storage media refer to anymedium or media that participate in providing instructions to a centralprocessing unit (CPU), a processor, a microcontroller, or the like. Suchmedia may take forms including, but not limited to, non-volatile andvolatile media such as optical or magnetic disks and dynamic memory,respectively. Common forms of computer-readable storage media include afloppy disk, a flexible disk, a hard disk, magnetic tape, any othermagnetic storage medium, a CD-ROM disk, digital video disk (DVD), anyother optical storage medium, RAM, PROM, EPROM, a FLASHEPROM, any othermemory chip or cartridge.

Computer program code for carrying out operations for aspects of thepresent technology may be written in any combination of one or moreprogramming languages, including an object oriented programming languagesuch as Java, Smalltalk, C++ or the like and conventional proceduralprogramming languages, such as the “C” programming language or similarprogramming languages. The program code may execute entirely on theuser's computer, partly on the user's computer, as a stand-alonesoftware package, partly on the user's computer and partly on a remotecomputer or entirely on the remote computer or server. In the latterscenario, the remote computer may be coupled with the user's computerthrough any type of network, including a local area network (LAN) or awide area network (WAN), or the connection may be made to an externalcomputer (for example, through the Internet using an Internet ServiceProvider).

The corresponding structures, materials, acts, and equivalents of allmeans or step plus function elements in the claims below are intended toinclude any structure, material, or act for performing the function incombination with other claimed elements as specifically claimed. Thedescription of the present technology has been presented for purposes ofillustration and description, but is not intended to be exhaustive orlimited to the present technology in the form disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the presenttechnology. Exemplary embodiments were chosen and described in order tobest explain the principles of the present technology and its practicalapplication, and to enable others of ordinary skill in the art tounderstand the present technology for various embodiments with variousmodifications as are suited to the particular use contemplated.

Aspects of the present technology are described above with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems) and computer program products according to embodiments of thepresent technology. It will be understood that each block of theflowchart illustrations and/or block diagrams, and combinations ofblocks in the flowchart illustrations and/or block diagrams, can beimplemented by computer program instructions. These computer programinstructions may be provided to a processor of a general purposecomputer, special purpose computer, or other programmable dataprocessing apparatus to produce a machine, such that the instructions,which execute via the processor of the computer or other programmabledata processing apparatus, create means for implementing thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

These computer program instructions may also be stored in a computerreadable medium that can direct a computer, other programmable dataprocessing apparatus, or other devices to function in a particularmanner, such that the instructions stored in the computer readablemedium produce an article of manufacture including instructions whichimplement the function/act specified in the flowchart and/or blockdiagram block or blocks.

The computer program instructions may also be loaded onto a computer,other programmable data processing apparatus, or other devices to causea series of operational steps to be performed on the computer, otherprogrammable apparatus or other devices to produce a computerimplemented process such that the instructions which execute on thecomputer or other programmable apparatus provide processes forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods and computer program products according to variousembodiments of the present technology. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof code, which comprises one or more executable instructions forimplementing the specified logical function(s). It should also be notedthat, in some alternative implementations, the functions noted in theblock may occur out of the order noted in the figures. For example, twoblocks shown in succession may, in fact, be executed substantiallyconcurrently, or the blocks may sometimes be executed in the reverseorder, depending upon the functionality involved. It will also be notedthat each block of the block diagrams and/or flowchart illustration, andcombinations of blocks in the block diagrams and/or flowchartillustration, can be implemented by special purpose hardware-basedsystems that perform the specified functions or acts, or combinations ofspecial purpose hardware and computer instructions.

While various embodiments have been described above, it should beunderstood that they have been presented by way of example only, and notlimitation. The descriptions are not intended to limit the scope of thetechnology to the particular forms set forth herein. Thus, the breadthand scope of a preferred embodiment should not be limited by any of theabove-described exemplary embodiments. It should be understood that theabove description is illustrative and not restrictive. To the contrary,the present descriptions are intended to cover such alternatives,modifications, and equivalents as may be included within the spirit andscope of the technology as defined by the appended claims and otherwiseappreciated by one of ordinary skill in the art. The scope of thetechnology should, therefore, be determined not with reference to theabove description, but instead should be determined with reference tothe appended claims along with their full scope of equivalents.

What is claimed is:
 1. A method, comprising: partitioning raw usagerecords in an object store of a compute-centric system using a map phasecontainer; grouping the partitioned records in the object store by user,type, and an identifier using a first set of reducer containers in thecompute-centric system; de-duplicating the grouped usage records in theobject store using a second set of reducer containers in thecompute-centric system; aggregating the de-duplicated usage records inthe object store for a user for a namespace into namespace records usingthe second set of reducer containers in the compute-centric system; andsumming namespace records for each namespace associated with the userinto a usage report using a third set of reducer containers in thecompute-centric system.
 2. A multitenant compute-centric system, thesystem comprising: one or more processors; a distributed object storecomprising a network service for storing data objects; and a pluralityof virtual machines implemented on logic encoded in one or more tangiblemedia for execution by the one or more processors, the plurality ofvirtual machines comprising: a map phase container that partitions rawusage records; a first set of reducer containers that groups partitionedrecords by user, type, and an identifier; a second set of reducercontainers that de-duplicate the grouped usage records; the second setof reducer containers aggregating the de-duplicated usage records for auser for a namespace into namespace records; and a third set of reducercontainers that each sum the namespace records for each namespaceassociated with a user into usage reports.
 3. The method according toclaim 1, further comprising obtaining raw usage records from shards thatcomprise data dumps for an object metadata store.
 4. The methodaccording to claim 1, wherein de-duplicating comprises locatingidentifiers that point to the same object.
 5. The method according toclaim 1, the method further comprising generating hourly usage reportsfor a user for a namespace generated on an hourly basis.
 6. The methodaccording to claim 1, wherein the method comprises metering object storeusage.
 7. The method according to claim 1, wherein a value field isextracted and emitted as output.
 8. The method according to claim 1,wherein the identifier is an object identifier.
 9. The method accordingto claim 1, wherein all identifiers that point to the same object for asingle owner are collocated on a single reducer.
 10. The methodaccording to claim 1, wherein keys are implemented, any keys that pointto the same object are de-duplicated, and usage per owner is aggregatedby namespace.
 11. The method according to claim 1, wherein identifiersare object identifiers that are indexed in memory.
 12. The methodaccording to claim 1, wherein de-duplicated records are output.
 13. Thesystem according to claim 2, wherein the map phase container extracts avalue field and emits it as output.
 14. The system according to claim 2,wherein the identifier is an object identifier.
 15. The system accordingto claim 2, wherein all identifiers that point to the same object for asingle owner are collocated on a single reducer.
 16. The systemaccording to claim 2, wherein each reducer container de-duplicates anykeys that point to the same object and aggregates usage per owner bynamespace.
 17. The system according to claim 2, wherein identifiers areobject identifiers that are indexed in memory.
 18. The system accordingto claim 2, wherein any of the first set of reducer containers, thesecond set of reducer containers, and the third set of reducercontainers are configured to output de-duplicated records.
 19. Themethod according to claim 5, further comprising summing namespacerecords for a plurality of namespaces associated with a plurality ofusers into an aggregate usage report via a fourth set of reducercontainers, the aggregate usage report indicating a total amount ofusage of an object store by the plurality of users.
 20. The methodaccording to claim 11, wherein any keys that point to the same objectonly increment a user's number of keys.
 21. The system according toclaim 17, wherein any keys that point to the same object only incrementa user's number of keys.